This invention is being disclosed in connection with cervical cancer. Uterine cervical cancer is the second most common cancer in women worldwide, with nearly 500,000 new cases and over 270,000 deaths annually (http://www-depdb.iarc.fr/globocan2002.htm, incorporated herein by reference) Colposcopy is a diagnostic method used to detect cancer precursors and cancer of the uterine cervix (B. S. Apgar, Brotzman, G. L. and Spitzer, M., Colposcopy: Principles and Practice, W.B. Saunders Company: Philadelphia, 2002, incorporated herein by reference). CAD (“computer aided diagnosis”) for colposcopy represents a new application of medical image processing. The inventors have developed a CAD system that mimics or emulates the diagnostic process used by colposcopists to assess the severity of abnormalities (Lange H. and Ferris, Daron G.; Computer-Aided-Diagnosis (CAD) for colposcopy; SPIE Medical Imaging 2005; SPIE Proc. 5747, 2005, incorporated herein by reference).
Colposcopists form colposcopic impressions based on different macroscopic epithelial (relating to membranes) features within cervical tissue. Colposcopic grading systems provide a structured, systematic method to critically analyze cervical findings. A systematic approach, once learned and mastered, becomes routine and is extraordinarily beneficial. Colposcopic grading systems also allow colposcopists to form more accurate colposcopic impressions. Well-designed colposcopic scoring systems enhance colposcopic reproducibility. Colposcopic grading systems are also helpful when attempting to select the most appropriate biopsy site, particularly when large, complex lesions (abnormal growths) of the cervix are encountered.
Scoring schemes, like the Reid's colposcopic index, are an aid for making colposcopic diagnoses (Reid R, Scalzi P. Genital warts and cervical cancer. VII. An improved colposcopic index for differentiating benign papillomaviral infection from high-grade cervical intraepithelial neoplasia. Am J Obstet Gynecol 1985; 153:611-618, incorporated herein by reference; Reid, R., Stanhope, C. R., Herschman, B. R., Crum, C. P., and Agronow, S. J., Genital warts and cervical cancer. IV. A colposcopic index for differentiating subclinical papillomaviral infection from cervical intraepithelial neoplasia, Am. J. Obstet. Gynecol. 149(8): 815-823. 1984, incorporated herein by reference) based on various features, including margin or border of lesion (abnormal growth), color of lesion following application of 5% acetic acid solution, blood vessel characteristics within the lesion, and response of the lesion to the application of Lugol's iodine solution. These features are individually assessed and scored before the scores of all features are combined to yield a composite score that grades disease severity. The Reid index differentiates low-grade cervical disease from high-grade disease. Consequently, the Reid Colposcopic Index (RCI) is not designed to discriminate premalignant from malignant cervical neoplasia. Nonetheless, the index provides a popular means of standardizing the evaluation of cervical neoplasia.
Rubin and Barbo (Rubin, M. M. and Barbo, D. M., Ch. 9a: Rubin and Barbo Colposcopic Assessment System, in Colposcopy: Principles and practice, eds. Apgar, B. S., Brotzman, G. L., and Spitzer, M., pp. 187-195. W.B. Saunders Company, Philadelphia, 2002, incorporated herein by reference) developed an assessment method that retains the best descriptors of some of the previous colposcopic grading systems, but eliminates the numbers, which can be confusing. In addition, it expands the system to include descriptors for normal findings. More importantly, it includes descriptors that focus the clinician's pattern recognition process on the possibility of microinvasive or frankly invasive disease. For example, it not only measures the intensity of the Acetowhite epithelial changes but also addresses other color-tone changes, such as red, yellow, and dull gray, that correlate more with the presence of invasive cancer.
Other factors that warrant consideration include a patient's age and the size and distribution of lesions. Cancer of the cervix is rare in patients younger than 25 years old. The majority of high-grade squamous intraepithelial disease (specifically CIN3) is found in women between 28 and 32 years of age. CIN1 lesions tend to be relatively small. The mean length of CIN1 lesions is approximately 2.8 mm (millimeters or thousandths of a meter). However, these lesions can reach a maximum length of 11.5 mm. In comparison, CIN2 and CIN3 lesions are larger. The mean (average) length of CIN2 and CIN3 are 5.8 mm and 7.6 mm respectively. Their maximum lengths are 18.2 mm and 20.6 mm respectively. Low-grade lesions may occupy only one quadrant of the cervix or a small percentage of the surface area of the ectocervix. The distribution of CIN1 varies from unifocal (lesion occurring at a single location) to multifocal (lesions occurring at multiple locations). Multiple, distinct, small, randomly scattered lesions are characteristic of CIN1.
CIN2 is invariably seen within the transformation zone (TZ). The TZ is the region of the cervix where the columnar epithelium has been replaced by the new metaplastic squamous epithelium. In contrast to CIN1, which may be found outside the TZ. CIN 2 may be multifocal, but a unifocal lesion is more common. Satellite lesions are not usually representative of CIN 2. Colposcopists will usually find CIN2 along the squamous-columnar junction (SCJ), located either on the ectocervix or within the endocervical canal.
CIN 3 lesions tend to be confluent (flowing together or blended into one), and longer and wider than CIN 1 or CIN 2 lesions. CIN 3 is usually located within the central portion of the cervix, inside the inner curve towards the external os. CIN 3 is rarely occult (hidden from the eye) when present on the ectocervix. The linear length of CIN 3 lesions, defined as the distance over the tissue surface between caudal (at or near the posterior end of the body) and cephlad (toward the head or anterior of the body) edges, varies between 2 mm to 22 mm. Mean linear lengths range from 6 mm to 10 mm. Long linear lesions—those greater than 10 mm, particularly when there is endocervical involvement—are always suspicious for cancer. As the surface area of lesions increases to more than 40 sq mm, so should the suspicion for cancer. It has been reported that the size of transformation zone (TZ), the size of lesions (abnormal growths), distinct margins, the vascular pattern (the pattern of blood vessels), and acetowhite color (color after the application of acetic acid) were significantly associated with the histological grade and demonstrated that the size of cervical lesion might be of clinical importance (Kierkegaard, O., Byralsen, C., Hansen, K. C., Frandsen, K. H., and Frydenberg, M., Association between colposcopic findings and histology in cervical lesions: the significance of the size of the lesion, Gynecol. Oncol. 57(1): 66-71. 1995, incorporated herein by reference).
Evidence based medicine (EBM) is defined as “the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients”. In EBM, human perception and interpretation are considered highly valuable in diagnostic interpretation. However, human assessment can be subjective and have low inter-observer agreement (Ferris, D. G. and Litaker, M., Interobserver agreement for colposcopy quality control using digitized colposcopic images during the ALTS trial, J. Low. Genit. Tract Dis. 9(1): 29-35, 2005, incorporated herein by reference; Jeronimo, J., Massad, L. S., Castle, P. E., Wacholder, S., and Schiffman, M., Interobserver Agreement in the Evaluation of Digitized Cervical Images, Obstet. Gynecol. 110(4): 833-840, 2007, incorporated herein by reference).
Computers can be programmed to perform reasoning tasks and can be used for solving diagnostic problems. Colposcopic grading systems have been used by colposcopists for more than 40 years to derive clinical diagnoses. Colposcopists derive the colposcopic features of a patient qualitatively through a visual exam using a colposcope, determine the extent of disease and initiate patient management based on their experience. The procedure takes about ten or fifteen minutes in a gynecologist's office. Due of the subjective nature of the exam, the accuracy of colposcopy is highly dependent upon the colposcopist's experience and expertise. A computer implemented colposcopic scoring algorithm that utilizes quantified colposcopic features is desirable to provide physicians a suggested diagnostic decision.
Most existing tissue classification systems rely on statistical approaches. One example is the Georgia Tech Vision (GTV) system (Dickman, E. D., Doll, T. J., Chiu, C. K., and Ferris, D. G., Identification of Cervical Neoplasia Using a Simulation of Human Vision, Journal of Lower Genital Tract Disease 5(3): 144-152, 2001, incorporated herein by reference), a generic computer vision system inspired by the human vision system, to recognize normal and abnormal cervical features. Another example is the Multimodel Hyperspectral Imaging (MHI) system for the noninvasive diagnosis of cervical neoplasia (Ferris, D. G., Lawhead, R. A., Dickman, E. D., Holtzapple, N., Miller, J. A., Grogan, S. et al., Multimodal hyperspectral imaging for the noninvasive diagnosis of cervical neoplasia, J. Low. Genit. Tract Dis. 5(2): 65-72, 2001, incorporated herein by reference). Recently, a reflectance fluorescence system used to differentiate high grade intraepithelial lesions from normal or low grade intraepithelial lesions has been reported (Park, S. Y., Follen, M., Milbourne, A., Rhodes, H., Malpica, A., Mackinnon, N. et al., Automated image analysis of digital colposcopy for the detection of cervical neoplasia, J. Biomed. Opt. 13(1): 014029-1-014029-10, 2008, incorporated herein by reference). Further, a successful optical system LUMA™ for in-vivo (in the living body) detection of high-grade cervical intraepithelial neoplasia has also been reported (Huh, W. K., Cestero, R. M., Garcia, F. A., Gold, M. A., Guido, R. S., McIntyre-Seltman, K. et al., Optical detection of high-grade cervical intraepithelial neoplasia in vivo: results of a 604-patient study, Am. J. Obstet. Gynecol. 190(5): 1249-1257. 2004, incorporated herein by reference; Kendrick, J. E., Huh, W. K., and Alvarez, R. D., LUMA Cervical Imaging System, Expert Review of Medical Devices 4(2): 121-129, 2007, incorporated herein by reference). In the LUMA™ system, diagnostic scans of the entire human cervix are performed following acetic acid application employing three integrated optical measurements: laser-induced fluorescence spectroscopy, white light diffuse reflectance spectroscopy, and video imaging. Further, multivariate classification algorithms were included to exploit spectral differences in the interaction of specific light sources with different cervical tissue types.
Another in vivo detection and quantitative grading system called DySIS™ (Dynamic Spectral Imaging System) has been reported recently (Forthphotonics, DySIS:Dynamic Spectral Imaging System, http://www.forth-photonics.gr/dysis.php?page=dysis-trials2008, incorporated herein by reference; Soutter, W. P., Diakomanolis, E., Lyons, D., and Haidopoulos, D., Dynamic Spectral Imaging—In Vivo Detection and Quantitative Grading of Cervical Neoplasia, ASCCP 2008 Meeting Abstract. Journal of Lower Genital Tract Disease 12(2): 160, 2008, incorporated herein by reference). DySIS™ is the first CE-marked (CE mark is a mandatory conformity mark on many products placed on the single market in the European Economic Area (EEA)—The CE marking certifies that a product has met EU health, safety, and environmental requirements, which ensure consumer safety) device to utilize both digital and dynamic spectral imaging technology for assisting automated (and user independent) detection and mapping of cervical neoplasia (lesions or tumors) in vivo.
The present invention is a computer aided detection technology based on a colposcopic grading system to accurately assess and identify low-grade lesions, high-grade lesions and cancer. It is different from statistical learning approaches listed in the prior art. The present invention is a rule based approach following colposcopy which accumulates the evidence of disease using morphological evaluation of epithelium and blood vessels. In statistical learning, a diagnostic decision is based on the statistical features derived from a huge amount of training data via cross validation. Therefore, it is a supervised learning approach based on training data and statistics theory. The current invention transfers the qualitative grading rules in modern colposcopy into a quantified computer program. It does not require any training from the data. It is an un-supervised approach based on prior medical knowledge. A flowchart showing an overview of the present invention is shown in FIG. 1. Prior to applying the tissue classification algorithm of the present invention, other methods are first applied to extract information which is used as the input to the tissue classification algorithm. The other methods include anatomic features algorithms, acetowhite feature extraction algorithms, mosaic and punctation detection algorithms (for example, W. Li and A. Poirson, Detection and characterization of abnormal vascular patterns in automated cervical image analysis, Lecture Notes in Computer Science—Advances in Visual Computing 4292, Second International Symposium, ISVC 2006, Lake Tahoe, Nev., November 2006 Proceedings, Part II, 627-636 (Springer 2006), incorporated herein by reference), and atypical vessels extraction algorithms (as disclosed in co-pending, commonly assigned patent application entitled “Methods for Detection and Characterization of Atypical Vessels in Cervical Imagery” filed Aug. 1, 2008, Ser. No. 12/221,328 incorporated herein by reference). The outputs of these methods serve as inputs for the present tissue classification method described herein. The tissue classification method is applied to image data of a cervix taken before and after applying acetic acid in real time during a clinical exam, as well as demographic data of the subject, such as age. The output of the tissue classification method includes a diagnostic decision with the following findings: the type of lesion (Normal/NED (“No Evidence of Disease”), Low-grade dysplasia, High-grade dysplasia, or cancer); disease location or the location of the high-grade (CIN 2 and CIN 3) lesions; and the confidence level of the decision.
The following patents and patent applications may be considered relevant to the field of the invention:
U.S. Pat. No. 7,309,867 to Costa et al., incorporated herein by reference, discloses methods for determining the probability that a given region of tissue sample contains tissue of given category, such as CIN, CIN II/III normal squamous, normal columnar, and metaplasia by utilizing a combination of statistical and non statistical classification techniques and by combining spectral data and image data.
U.S. Pat. No. 7,310,547 to Zelenchuk, incorporated herein by reference, discloses a system and method for the in situ discrimination of health and diseased tissue. It includes a fiberoptic probe to direct ultraviolet illumination onto a tissue specimen and to collect the fluorescent response radiation. The response radiation is observed at three selected wavelengths, one of which corresponds to an isosbestic point. In one example, the isosbestic point occurs at about 431 nm. The intensities of the observed signals are normalized using the 431 nm intensity. A score is determined using the ratios in a discriminant analysis. The tissue under examination is resected or not, based on the diagnosis of disease or health, according to the outcome of the discriminant analysis.
U.S. Pat. No. 7,260,248 to Kaufman et al., incorporated herein by reference, discloses methods of relating a plurality of images based on measures of similarity. The methods are useful in segmenting a sequence of colposcopic images of tissue. The methods can be applied to determine tissue characteristics in acetowhitening testing of cervical tissue. It discloses a method of determining a tissue characteristic that includes obtaining a plurality of images of a tissue; determining a relationship between two or more regions in the images; segmenting the images based on the relationship; and determining a characteristic of the tissue based on the segmentation. The determining step includes characterizing the tissue as either normal, CIN I, CIN II, CIN III or CIN II/III.
U.S. Pat. No. 6,766,184 to Utzinger et al., incorporated herein by reference, discloses methods and an apparatus for generating multispectral images of tissue. The image may be used for cervical cancer detection. A primary radiation is produce with a illumination source and filtered to select a first wavelength and first polarization. Tissue is illuminated with the filtered primary radiation to generate a secondary radiation, which is filtered to select a second wavelength and second polarization. The filtered secondary radiation is collected with a detector, and a plurality of multispectral images of the tissue is generated according to different combination of the first and second wavelengths and polarizations.
U.S. Pat. No. 6,198,838 to Roehrig et al., incorporated herein by reference, discloses a method and system for detecting suspicious portions of digital mammograms by using independently calculated mass and spiculation information. The method is used in a computer aided diagnosis system that is designed to bring suspicious or possibly cancerous lesions in fibrous breast tissue to the attention of a radiologist or other medical professional. In a preferred embodiment, spiculation information and mass information are independently calculated, with the computed spiculation information not being dependent on results of the mass information computation, thus leading to greater reliability.
U.S. Pat. No. 6,135,965 to Tumer et al., incorporated herein by reference, discloses an apparatus and method for spectroscopic detection of tissue abnormality in cervical tissue using neural networks to analyze in vivo measurements of fluorescence spectra.
U.S. Pat. No. 5,857,030 to Gaborski et al., incorporated herein by reference, discloses an automated method and system for digital image processing of radiologic images including a pre-processing stage of filtering, preliminary selection phase of segmentation, and a pattern classification phase that includes neural network classification.
U.S. Pat. No. 5,982,917 to Clarke et al., incorporated herein by reference, discloses a computer-assisted diagnostic (CAD) method and apparatus for the enhancement and detection of suspicious regions in digital x-ray images.
U.S. Patent Publication No. 2006/0141633 to Balas, incorporated herein by reference, discloses a method and apparatus for in vivo detection and mapping of alterations caused by biochemical and/or functional characteristics of epithelial tissues during the development of cancer.
U.S. Patent Publication No. 2006/0184040 to Keller et al., incorporated herein by reference, discloses a method and device for detecting a tissue abnormality whereby the method comprises emitting light from a light source onto the tissue; directing light emitted reflected from the tissue via the optics to the multiple wavelength imaging optical subsystem, and isolating one or more wavelengths or wavelength bands of interest; directing the one or more wavelengths or wavelength bands of interest to the one or more imaging devices, and using the devices to record images of the one or more wavelengths or wavelength bands of interest; transferring image data from the images to a computational system; and analyzing the images for one or more spectral patterns associated with tissue abnormalities.
U.S. Patent Publication No. 2008/0058593 to Gu et al., incorporated herein by reference, discloses a process for providing computer aided diagnosis from video data of an organ during an examination with an endoscope, comprising analyzing and enhancing image frames from the video and detecting and diagnosing any lesions in the image frams in real time during the examination. Further, the image data may be used to create a three-dimensional reconstruction of the organ.